scholarly journals A Discrete-Binary Transformation of the Reliability Redundancy Allocation Problem

2015 ◽  
Vol 2015 ◽  
pp. 1-6
Author(s):  
Marco Caserta ◽  
Stefan Voß
Keyword(s):  
Dynamic Programming ◽  
Optimization Problem ◽  
Allocation Problem ◽  
Programming Approach ◽  
Redundancy Allocation Problem ◽  
Redundancy Allocation ◽  
Dynamic Programming Approach ◽  
Integer Problem ◽  
Redundancy Optimization

Given a reliability redundancy optimization problem in itsdiscreteversion, it is possible to transform such integer problem into a correspondingbinaryproblem in log-time. A simple discrete-binary transformation is presented in this paper. The proposed transformation is illustrated using an example taken from the reliability literature. An immediate implication is that a standard exact dynamic programming approach may easily solve instances to optimality that were usually only solved heuristically.

Modeling M Warehouse N Manpower-Team Allocation Problem Using Dynamic Programming Approach

2019 ◽  
Vol 10 (4) ◽  
pp. 100-112
Author(s):  
Mohit Goswami
Keyword(s):  
Dynamic Programming ◽  
Performance Measure ◽  
Allocation Problem ◽  
Programming Approach ◽  
Problem Structure ◽  
Detailed Model ◽  
Dynamic Programming Approach ◽  
Workforce Allocation ◽  
Study Results ◽  
Dynamic Programming Problem

In this research, a dynamic programming-based approach is deployed to model and solve the manpower allocation problem for warehouses. The authors specifically evolve the detailed model for M warehouses and N teams (available for allocation to these warehouses). Profitability is considered as a performance measure for the allocation problem. The warehouses and manpower-team are modelled as stages and states respectively within the dynamic programming problem structure. Owing to the rather abstract nature of such allocation problems possessing Markovian properties and having similarities with stage-gate type of a problem, dynamic programming approach is deployed. The study results in recommending key decisions in workforce allocation for organizations such as retailers operating multiple warehouses.

Non-linear threshold algorithm based solution for the redundancy allocation problem considering multiple redundancy strategies

2019 ◽  
Vol 25 (3) ◽  
pp. 397-411 ◽  
Author(s):  
Nabil Nahas ◽  
Mohamed N. Darghouth ◽  
Abdul Qadar Kara ◽  
Mustapha Nourelfath
Keyword(s):  
Optimization Problem ◽  
Allocation Problem ◽  
Computational Time ◽  
Redundancy Allocation Problem ◽  
Redundancy Allocation ◽  
Content Type ◽  
Linear Threshold ◽  
Subsystem Level ◽  
Non Linear ◽  
Selection Of

Purpose The purpose of this paper is to introduce an efficient algorithm based on a non-linear accepting threshold to solve the redundancy allocation problem (RAP) considering multiple redundancy strategies. In addition to the components reliability, multiple redundancy strategies are simultaneously considered to vary the reliability of the system. The goal is to determine the optimal selection of elements, redundancy levels and redundancy strategy, which maximizes the system reliability under various system-level constraints. Design/methodology/approach The mixed RAP considering the use of active and standby components at the subsystem level belongs to the class of NP-hard problems involving selection of elements and redundancy levels, to maximize a specific system performance under a given set of physical and budget constraints. Generally, the authors recourse to meta-heuristic algorithms to solve this type of optimization problem in a reasonable computational time, especially for large-size problems. A non-linear threshold accepting algorithm (NTAA) is developed to solve the tackled optimization problem. Numerical results for test problems from previous research are reported and analyzed to assess the efficiency of the proposed algorithm. Findings The comparison with the best solutions obtained in previous studies, namely: genetic algorithm, simulated annealing, memetic algorithm and the particle swarm optimization for 33 different instances of the problem, demonstrated the superiority of the proposed algorithm in finding for all considered instances, a high-quality solution in a minimum computational time. Research limitations/implications Considering multiple redundancy strategies helps to achieve higher reliability levels but increases the complexity of the obtained solution leading to infeasible systems in term of physical design. Technological constraints must be integrated into the model to provide a more comprehensive and realistic approach. Practical implications Designing high performant systems which meet customer requirements, under different economic and functional constraints is the main challenge faced by the manufacturers. The proposed algorithm aims to provide a superior solution of the reliability optimization problem by considering the possibility to adopt multiple redundancy strategies at the subsystem level in a minimum computational time. Originality/value A NTAA is expanded to the RAP considering multiple redundancy strategies at the subsystem level subject to weight and cost constraints. A procedure based on a penalized objective function is developed to encourage the algorithm to explore toward the feasible solutions area. By outperforming well-known solving technique, the NTAA provides a powerful tool to reliability designers of complex systems where different varieties of redundancies can be considered to achieve high-reliability systems.

scholarly journals AN OPTIMAL CONSUMPTION AND INVESTMENT PROBLEM WITH QUADRATIC UTILITY AND SUBSISTENCE CONSUMPTION CONSTRAINTS: A DYNAMIC PROGRAMMING APPROACH

2018 ◽  
Vol 23 (4) ◽  
pp. 627-628 ◽  
Author(s):  
Yong Hyun Shin ◽  
Jung Lim Koo ◽  
Kum Hwan Roh
Keyword(s):  
Dynamic Programming ◽  
Optimization Problem ◽  
Sufficient Conditions ◽  
Dynamic Programming Method ◽  
Programming Approach ◽  
Optimal Consumption ◽  
Dynamic Programming Approach ◽  
Investment Problem ◽  
Optimal Consumption And Investment ◽  
Consumption Constraints

In this paper, we analyze the optimal consumption and investment problem of an agent who has a quadratic-type utility function and faces a subsistence consumption constraint. We use the dynamic programming method to solve the optimization problem in continuous-time. We further provide the sufficient conditions for the optimization problem to be well-defined.

scholarly journals A Novel Dynamic Programming Approach for Optimizing Driving Strategy of Subway Trains

2020 ◽  
Vol 325 ◽  
pp. 01002
Author(s):  
Hao Gao ◽  
Yadong Zhang ◽  
Jin Guo
Keyword(s):  
Dynamic Programming ◽  
Optimization Problem ◽  
Cost Functions ◽  
Programming Approach ◽  
Safe Driving ◽  
Dynamic Programming Approach ◽  
Urban Rail ◽  
Rail Line ◽  
Optimal Driving

The reduction of operation energy consumption without decreasing service quality has become a great challenge in subways daily operation. A novel DP based approach is proposed for optimizing the train driving strategy. The optimal driving problem is first considered as a multi-objective problem with five optimal targets (i.e., energy saving, punctual arriving, less switching, safe driving and accurate stopping). The optimization problem is remodelled as a multistage decision problem by discretizing the continuous train movement in space. The process of dynamic programming is carried out in the velocity-space status space. Due to the discretizing rules of searching space, the optimal goals of safe driving and accurate stopping can be satisfied during the searching process. The rest of multiple goals are spilt into cost functions and constrains for each stage. Due to the multiple cost functions, a set of pareto optimal solutions can be achieved at each vertex during the process of dynamic programming. To further improve the efficiency of algorithm, two evaluation criterions are introduced to maintain the capacity of the pareto set at each vertex. A case study of Yizhuang urban rail line in Beijing is conducted to verify the effectiveness and the efficiency of DP based algorithms.

Sign in / Sign up

Export Citation Format

Share Document